{ "ok": true, "resource": { "id": "RES_46439CB31EC5", "resource_type": "skill", "canonical_url": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/autoskill", "name": "autoskill", "summary_en": "Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.", "summary_zh": "通过 screenpipe 观察用户的屏幕，检测重复的研究工作流，将其与现有的 scientific-agent-skills 进行匹配，并为尚未覆盖的模式起草新技能（或组合现有技能的编排配方）。当用户要求分析其近期工作并根据实际行为提出技能建议时使用。需要本地运行 screenpipe 守护进程（https://github.com/screenpipe/screenpipe）并监听 3030 端口——该技能没有其他数据源，如果 screenpipe 不可达将拒绝运行。所有检测均在本地进行；只有经过脱敏处理的聚类摘要才会发送给 LLM。", "description_en": "autoskill is an Agent Skill that analyzes a user's recent on-screen activity (captured by the local screenpipe daemon) to identify repeated research workflows, matches them against the existing 135 skills in the scientific-agent-skills repository, and drafts new skills or composition recipes for uncovered patterns. It requires a locally running screenpipe daemon on localhost:3030 with a valid SCREENPIPE_TOKEN; without it, the skill raises ScreenpipeUnreachable. The skill performs all detection and clustering locally, redacts sensitive data (emails, API keys, tokens, phone numbers) before any LLM call, and defaults to a local LLM backend (LM Studio) to keep data on-machine. Cloud backends (Claude, Foundry) are opt-in. The skill is invoked on-demand by the user with a time window, and outputs a staged folder of proposals (report.md, new-skills/, composition-recipes/) for user review and promotion. It includes a doctor subcommand for preflight checks, a run subcommand for the pipeline, and a promote subcommand to move approved proposals into the skills directory. The skill is part of the Scientific Agent Skills library by K-Dense, licensed under MIT, and cites a related arXiv paper when used in research outputs.", "description_zh": "autoskill 是一个 Agent Skill，用于分析用户近期屏幕活动（由本地 screenpipe 守护进程捕获），识别重复的研究工作流，将其与 scientific-agent-skills 仓库中现有的 135 个技能进行匹配，并为未覆盖的模式起草新技能或组合配方。它要求本地运行 screenpipe 守护进程（localhost:3030）并提供有效的 SCREENPIPE_TOKEN；否则技能将抛出 ScreenpipeUnreachable 异常。该技能的所有检测和聚类均在本地进行，在任何 LLM 调用之前对敏感数据（电子邮件、API 密钥、令牌、电话号码）进行脱敏处理，并默认使用本地 LLM 后端（LM Studio）以保持数据不出机器。云后端（Claude、Foundry）为可选。该技能由用户按需调用，指定时间窗口，输出一个分阶段的提案文件夹（report.md、new-skills/、composition-recipes/）供用户审查和提升。它包含 doctor 子命令用于预检，run 子命令用于执行流水线，promote 子命令用于将批准的提案移动到技能目录。该技能是 K-Dense 的 Scientific Agent Skills 库的一部分，采用 MIT 许可，并在用于研究输出时引用相关 arXiv 论文。", "provider_name": "K-Dense-AI", "homepage_url": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/autoskill", "endpoint_url": null, "source_url": 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